Edge AI Engineering 📱

Optimized machine learning models for edge and mobile devices. Showcasing efficient model deployment, optimization techniques, and real-world edge AI applications.

Features • Installation • Quick Start • Documentation • Contributing

📑 Table of Contents

• Features
• Project Structure
• Prerequisites
• Installation
• Quick Start
• Documentation
  • Models
  • Optimization
  • Benchmarks
• Contributing
• Versioning
• Authors
• Citation
• License
• Acknowledgments

✨ Features

• Model quantization and optimization
• Mobile-first architectures
• Battery-efficient inference
• Cross-platform deployment
• Edge-optimized pipelines

📁 Project Structure

graph TD
    A[edge-ai-engineering] --> B[models]
    A --> C[optimization]
    A --> D[deployment]
    A --> E[benchmarks]
    B --> F[tflite]
    B --> G[pytorch-mobile]
    C --> H[quantization]
    C --> I[compression]
    D --> J[android]
    D --> K[ios]
    E --> L[performance]
    E --> M[battery]

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Click to expand full directory structure

edge-ai-engineering/
├── models/            # Model implementations
│   ├── tflite/       # TensorFlow Lite models
│   └── pytorch/      # PyTorch Mobile models
├── optimization/      # Optimization tools
│   ├── quantization/ # Model quantization
│   └── compression/  # Model compression
├── deployment/       # Platform-specific deployment
│   ├── android/     # Android deployment
│   └── ios/         # iOS deployment
├── benchmarks/       # Performance testing
└── README.md         # Documentation

🔧 Prerequisites

• Python 3.8+
• TensorFlow Lite 2.14+
• PyTorch Mobile 2.2+
• Android SDK/NDK
• Xcode (for iOS)

📦 Installation

# Clone repository
git clone https://github.com/BjornMelin/edge-ai-engineering.git
cd edge-ai-engineering

# Create environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

🚀 Quick Start

from edge_ai import optimization, deployment

# Optimize model for mobile
optimized_model = optimization.quantize_for_mobile(
    model,
    target_platform="android",
    quantization="int8"
)

# Deploy to device
deployment = deployment.MobileDeployment(
    model=optimized_model,
    platform="android",
    optimize_battery=True
)

# Generate deployment package
deployment.export()

📚 Documentation

Models

Model	Task	Size	Latency (ms)
MobileNetV3	Classification	4MB	15
TinyYOLO	Detection	8MB	25
MobileViT	Vision	6MB	20

Optimization

• Int8 quantization
• Model pruning
• Architecture optimization
• Memory footprint reduction

Benchmarks

Performance on different devices:

Device	Model	Battery Impact	FPS	Memory
Pixel 6	MobileNet	2%/hr	30	120MB
iPhone 13	TinyYOLO	3%/hr	25	150MB
RPi 4	MobileViT	N/A	15	200MB

🤝 Contributing

• Contributing Guidelines
• Code of Conduct
• Development Guide

📌 Versioning

We use SemVer for versioning. For available versions, see the tags on this repository.

✍️ Authors

Bjorn Melin

• GitHub: @BjornMelin
• LinkedIn: Bjorn Melin

📝 Citation

@misc{melin2024edgeaiengineering,
  author = {Melin, Bjorn},
  title = {Edge AI Engineering: Optimized Mobile Machine Learning},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/BjornMelin/edge-ai-engineering}
}

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• TensorFlow Lite team
• PyTorch Mobile developers
• Mobile ML community
• Edge computing researchers

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Made with 📱 and ❤️ by Bjorn Melin